System and method for quantifying meeting effectiveness using natural language processing

ABSTRACT

Systems, methods, and computer-readable storage media for quantifying meeting effectiveness for an individual. A system configured as disclosed herein uses data from multiple meetings in which a user participated to create a user profile for the user. The system then receives data related to a new meeting in which the user participated, processes the new meeting data into segments using natural language processing, tags the resulting segments based on contexts, and compares the tagged segments to the user profile to generate a meeting effectiveness score for the new meeting which is specific to the user. The system can use machine learning to iteratively improve an ability of the system to generate the tagged segments using historical meeting data and updating that historical meeting data with each iteration of scoring a meeting&#39;s effectiveness.

BACKGROUND 1. Technical Field

The present disclosure relates to quantifying meeting effectivenessusing natural language processing, and more specifically to quantifyinghow effective a meeting was for a participant word usage (obtainedthrough speech-to-text processing and natural language processing) anduser profiles.

2. Introduction

Metrics regarding meeting participation and efficiency do not take intoaccount the actual speaking time of the participants, words used byparticipants, and other quantitative aspects of the meeting. Because ofthis lack of quantification, meeting participation and efficiency isoften incorrectly estimated using guesswork rather than being based onquantifiable, collected data.

SUMMARY

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Disclosed are systems, methods, and non-transitory computer-readablestorage media a technical solution to the technical problem described. Amethod for performing the concepts disclosed herein can include:receiving, at a server, meeting data for a plurality of meetings inwhich a first user participated, the meeting data comprising transcriptsfor each meeting in the plurality of meetings; generating, via aprocessor associated with the server and based on the transcripts, afirst user profile for the first user; receiving additional meeting datafor a new meeting in which the first user participated; processing, viathe processor executing natural language processing algorithms, theadditional meeting data into segments; tagging, via the processor, thesegments based on respective contexts, resulting in tagged segments;comparing, via the processor, the tagged segments to the first userprofile, resulting in a comparison; generating, via the processor basedon the additional meeting data and the comparison, a meetingeffectiveness score of the additional meeting data for the first user;and using, via the processor, machine learning to iteratively improve anability of the processor to generate the tagged segments within the newmeeting based on at least one of a context of the new meeting, aspeaking party within the new meeting, a relative time period within thenew meeting, or a style of presentation, wherein the machine learningaccesses a database of historical meeting data, and wherein with eachiteration of meeting analysis additional meeting data is saved in thedatabase of historical meeting data.

A system configured to perform the concepts disclosed herein can includea processor; and a non-transitory computer-readable storage mediumhaving instructions stored which, when executed by the processor, causethe processor to perform operations comprising: receiving audio inputfrom a meeting between a plurality of individuals; performingspeech-to-text processing on the audio input, resulting in a transcriptof the audio input; performing natural language processing on the audioinput using audio footprints of known users, resulting in (1) anidentification of a plurality of speakers during the meeting, and (2) aplurality of audio segments, each audio segment in the plurality ofaudio segments associated with at least one speaker in the plurality ofspeakers; identifying, based on the transcript and the plurality ofaudio segments, a context for each audio segment, resulting in contextsof the plurality of audio segments; receiving a user profile for a userwho attended the meeting; comparing the user profile to meeting dataassociated with the meeting, the meeting data comprising the pluralityof audio segments and the contexts of the plurality of audio segments,resulting in a comparison; generating a meeting score based on thecomparison, the meeting score indicative of how effective the meetingwas for the user.

A non-transitory computer-readable storage medium configured asdisclosed herein can have instructions stored which, when executed by acomputing device, cause the computing device to perform operations whichinclude at a first time: receiving, from a user, at least one meetingeffectiveness metric; transmitting the at least one meetingeffectiveness metric to a server; at a second time, after a firstmeeting which occurs after the first time: receiving, from the server, ameeting effectiveness score, wherein the meeting effectiveness score isgenerated by: performing natural language processing on audio input fromthe first meeting, resulting in a transcript of the audio input;performing natural language processing on the audio input using audiofootprints of known users, resulting in an identification of a pluralityof speakers during the first meeting; segmenting the audio input basedupon when the plurality of speakers speak during the first meeting,resulting in audio segments; identifying, based on topics discussedduring each audio segment in the audio segments, contexts for each audiosegment; comparing a user profile of the user to the audio segments andto the contexts of the plurality of audio segments, resulting in acomparison; and generating the meeting effectiveness score of the firstmeeting based on the comparison; and modifying a scheduled meeting basedon the meeting effectiveness score of the first meeting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of meeting profile creation for anindividual;

FIG. 2 illustrates an example of scoring a single meeting'seffectiveness for two different individuals;

FIG. 3 illustrates an example of quantifying a meeting and comparingthose metrics to a user profile;

FIG. 4 illustrates an example method embodiment; and

FIG. 5 illustrates an example computer system.

DETAILED DESCRIPTION

Various embodiments of the disclosure are described in detail below.While specific implementations are described, it should be understoodthat this is done for illustration purposes only. Other components andconfigurations may be used without parting from the spirit and scope ofthe disclosure.

The present disclosure addresses how to quantifiably determine ameeting's effectiveness for an individual without the subjectivityinherent when human beings try to determine a meeting's effectiveness.Systems, methods, and computer-readable media are disclosed which useinformation about how a user has previously participated in meetings andinformation provided by the user about their current goals or objectivesto create a user meeting profile, the user profile containingdata/statistics about what makes a meeting effective for that user.

As the user participates in future meetings, data about a specificmeeting can be extracted from media such as audio or video, thencompared to the user meeting profile to quantifiably determine if themeeting was effective for the user. Extracting data from the media can,for example, entail using speech to text processing to convert theaudio/video recording to an electronic transcript. Additional meetingdata, in the form of already executed transcripts, meeting notes,emails, etc., can, in some circumstances, also be used as inputs(electronic transcripts) to the system. The system can then useprocessor based natural language processing on the resulting electronictranscript to identify syntax, prosody (relying on timestamps within theelectronic transcripts), vocabulary, and other aspects of how the usercommunicates. The system can also execute a statistical analysis onaspects of the user's speech identified by the natural languageprocessing, then use the data from the natural language processing andthe statistical analysis to generate a meeting profile for theindividual user, with the meeting profile identifying how the userprefers to interact in meetings. The meeting profile can also includegoals, criteria, or other factors entered by the user themselves. Forexample, the user can indicate what they value in meetings, what theirgoals are, if there are specific individuals (or characteristics ofindividuals) that they do not enjoy, etc.

When the user participates in a new meeting, the system can then comparethe user's meeting profile to an electronic transcript of the meeting,where the electronic transcript is subjected natural language processingand/or statistical analysis, to generate a meeting effectiveness scorewhich is tailored to the user based on the user's profile and based onwhat was said during the meeting, who spoke during the meeting, who waspresent/participated in the meeting, etc.

In some cases, the meeting effectiveness score can then be used tomodify scheduled future meetings by sending out electronic modificationsto already distributed electronic meeting invitations. For example, ifafter a meeting the meeting effectiveness scores were poor for themajority of the individuals in the meeting, the system can change thetopic, agenda, order of speakers, time of meeting, participants, etc.,to change the already distributed electronic meeting data such as theelectronic meeting invitations.

In addition, in some configurations the system can use machine learningto iteratively improve itself using the meeting effectiveness score.Specifically, the machine learning process deployed by the system can,with each meeting effectiveness evaluation/score, access a databasestoring previous meeting information, compare the current meetinginformation to the previous meeting information stored in the database,and change how the processor of the system evaluates aspects of themeeting such as context, speakers, specific times within the meeting(such as a five minute window after a participant arrives, or while aspecific context is being discussed), styles of presentation, etc. Thechange to the processor code can occur directly within the processor orwithin a non-transitory computer-readable device electronicallyconnected to the processor and from which the processor obtainsexecutable code. In practice, modification of the processor code canoften take the form of modifying the weights at which the variousfactors of meeting effectiveness are evaluated by the processor, wherethe weights are adapted based on meeting effectiveness scores of one ormore users, user behavior (e.g., did the user modify aspects of theirmeeting profile immediately upon seeing a meeting effectiveness score?If so, that may indicate the user perceives a flaw in how the score isgenerated), future meetings organized by the participants, etc.

These and other variations shall be further described herein as thevarious illustrates are described. The disclosure now turns to FIG. 1 .

FIG. 1 illustrates an example of meeting profile 114 creation for anindividual 102. In this example, the system obtains data about multiplemeetings 104, 106, 108 in which the user 102 has participated. Examplesof the data can include electronic transcripts of the meetings, wherethe electronic transcripts are generated using audio from the meetingsand subjecting that audio to speech-to-text processing. The meeting datacan also include tagged segments from the transcripts, the taggedsegments having one or more metadata tags which identify some aspect ofthe segment. For example, a segment may be from when a particularparticipant is speaking, and the metadata tag for a segment may indicatethat participant was the speaker during that segment. Other potentialtags can include the context for the segment, who was present within themeeting, and/or other aspects of the meeting featured in the particularportion of the meeting represented by a given segment.

In an exemplary embodiment, a list of potential tags can bepre-configured for the particular implementation. For example, “fraud”and “banking” may be tags provided in a set of tags for use at afinancial institution. Other implementations may include tags relevantto that use case. The list of potential tags may also initially be emptyand added to via machine learning. Along those lines, words not in thisinitial set, but that come up frequently across the enterprise(excluding “the”, “and”, etc.) may also be included as potential tags.This may be done by machine learning, tracking the number of times aword is encountered, etc. Thus, the list of potential tags may bedynamic. The system may attempt to extract all tags detected anddetermines necessity by those that match the dynamic list.

The system stores this data in one or more databases of meeting history110. Preferably, only the tagged segments are stored within thedatabase, though in some configurations storing the original audioand/or video can occur. The tags may be stored in a database, andarranged in their own table. For example, the database may include arelation indicating “fraud in segment 34239”; and a second “banking insegment 34239”; and so on, for all segments stored.

The system uses the meeting data stored in the database of meetinghistory 110 as inputs to the meeting profile creator 112, which can be adistinct processor, component, or other module of the system, or whichcan be specific code executed by a processor to manipulate the meetingdata provided into a meeting profile 114 which is specific to the user102.

FIG. 2 illustrates an example of scoring a single meeting's 206effectiveness for two different individuals 202, 204. In this example,individual meeting profiles for both A 202 and B 204 have already beencreated by the system according to the process illustrated in FIG. 1 .As illustrated in FIG. 2 , both individuals A 202 and B 204 participatein a meeting #4, and the system generates data about the fourth meeting208. The system retrieves two separate meeting profiles 210, 212, one ofreach individual 202, 204, then individually compares 214, 216 themeeting data 208 to the respective profiles, resulting in a meetingeffectiveness score A 218 for user A 202, and a meeting effectivenessscore B 220 for user B.

The process for generating scores may be configurable based onparticular needs. In one example, a score may be the probability that ameeting influenced an individual multiplied by the determined magnitudeof influence. This probability may be determined based on detecting anychange in how an individual is speaking/acting in the future (from NLP).The magnitude may be be from the significance of this.

Consider an example. Suppose user A talks about the tag “AWS” 2 times onaverage, per day. After meeting number 1000, A now talks about “AWS” 6times on average, per day. We can say the magnitude of this was “3×” asit influenced their frequency of talking about AWS 3×. However, user Amay have attended many meetings besides meeting 1000, so perhaps meeting1000 was or was not the sole cause of their increased frequency. Thus,the tags in meeting 1000 are compared to other meetings that user A alsoattended. If all of these other meetings never mentioned AWS, and 1000mentioned it 40 times. A high probability (0.99) may be assigned thatmeeting 1000 was responsible for their increase. Thus, meeting 1000 hasa score of 0.99*3 for the user. Alternatively, it may be determined thatmeeting 1000 was just one of many meetings discussing AWS, and othermeetings mentioned AWS even more. This could give meeting 1000 0.20probability of influence, so the score is 0.2*3 for user A.

In some configurations, the generation of the meeting effectivenessscores 218, 220 can occur via two parallel processes, thereby reducingthe amount of time required to identify meeting effectiveness for theindividuals 202, 204. In other configurations, the respective meetingeffectiveness scores 218, 220 can be calculated sequentially, which maytake longer but reduce the amount of power the system is required toexert in a given moment.

FIG. 3 illustrates an example of quantifying a meeting and comparingthose metrics to a user profile. In this example, meeting data 302 isreceived by the system. Examples of meeting data 302 can includeelectronic transcripts of meetings between multiple participants. In thecase of a conference call, the voice transcript can be converted fromaudio to text using speech-to-text processing, and the system canreceive the produced electronic transcript as the meeting data. Includedwithin the electronic transcript of the conference call can beadditional data, such as when participants joined or left the call,which participants were speaking at a given time, any chat/text messagesshared during the conference call, slides or illustrates shared duringthe call, etc. In some configurations, and based on individual privacysettings, information about the behavior of the participants during theconference call can be included in the meeting data, such as if aparticipant was taking notes, browsing the Internet, on mute and on asecond call simultaneously, etc.

In the case of a meeting where participants are physically present,audio of the meeting can be processed in a similar manner to that of aconference call. If video of the meeting is available, the audio of themeeting can likewise be processed in a similar manner to that of aconference call, however the system can also do image-based processingto identify actions of specific users within the meeting, such asstanding up, walking around, pointing to people and/or objects, etc.,with those additional actions identified included as meeting data.

In the case of a group chat or chat conference, the meeting data can bethe text transcript of the chat, as well as any data about what slidesor other materials are being presented within the chat. In necessarycircumstances, the system can perform OCR (Optical CharacterRecognition) on any slides, frames of video, or other graphicspresented, thereby extracting text which can be analyzed by the systemas disclosed herein.

Meeting data can be gathered from databases and/or directly from ameeting input source (such as microphones, video cameras, speech-to-textprocessors, etc. The meeting data can then be parsed using naturallanguage processing with machine learning algorithms 304, which canidentify, from the meeting data, aspects of different parts of themeeting, such as context of the meeting at a given moment, vocabularyused, syntax of the speaker, etc. This parsed transcript information canbe divided into segments based on one or more factors. For example, ameeting can be divided into minute segments, with each minute segmenthaving an identified context, information about who is speaking, who ispresent, what information is being shared, etc. As another example,segments can be created for each sentence spoken within the meeting,whereas in another configuration segments can be created each time thespeaker within a meeting changes, such that a segment ends, and a newsegment begins every time there is a change of speaker within themeeting. In yet another example, the system can determine when thecontext of a meeting changes, and divide the meeting into segments basedon a change of context.

The system can iteratively improve how the segments are identified anddivided using machine learning, where the code the processor executes ismodified to either (1) provide improved segments, or (2) speed up therate at which the segments are recognized and divided. In both cases,the system executes the machine learning by identifying common patternswithin the segments identified and reducing the number of steps requiredfor the processor to identify the common patterns identified.

With the segments divided, the system can assign tags to the specificsegments 306, with the tags assigned based on data identified within thesegment. In some cases, as described above, that data can be used tocreate the segment, and the tags assigned correspond to the reason forthe specific start/stop points of the segment. In other cases, thesegment may be identified by the system for a specific reason with othertags assigned which are unrelated to the reason for the segmentstart/stop points. For example, in one situation a new segment may begenerated each time a new speaker begins speaking, and tags can be addedto the segments identifying who the speaker is, what the context of thesegment is, who were the other participants, etc. In another example, anew segment can be generated each time the system determines that thecontext of the meeting has shifted (determined by vocabulary, pauses,change of speaker, etc.), such that the segment may contain dialoguefrom multiple individuals, and the overall segment has tags identifyingaspects such as the overall context, participants, etc., as well as timerelated tags (timestamps) of the segment.

FIG. 4 illustrates an example method embodiment. The steps outlinedherein are exemplary and can be implemented in any combination thereof,including combinations that exclude, add, or modify certain steps. Asillustrated, a system (such as a server) receives meeting data for aplurality of meetings in which a first user participated, the meetingdata comprising transcripts for each meeting in the plurality ofmeetings (402), and generates, via a processor associated with theserver and based on the transcripts, a first user profile for the firstuser (404). The system receives additional meeting data for a newmeeting in which the first user participated (406) and processes, viathe processor executing natural language processing algorithms, theadditional meeting data into segments (408). The system identifiescontexts of these segments, and tags, via the processor, the segmentsbased on respective contexts, resulting in tagged segments (410). Thesystem compares, via the processor, the tagged segments to the firstuser profile, resulting in a comparison (412), and generates, via theprocessor based on the additional meeting data and the comparison, ameeting effectiveness score of the additional meeting data for the firstuser (414). The system then uses, via the processor, machine learning toiteratively improve an ability of the processor to generate the taggedsegments within the new meeting based on at least one of a context ofthe new meeting, a speaking party within the new meeting, a relativetime period within the new meeting, or a style of presentation, whereinthe machine learning accesses a database of historical meeting data, andwherein with each iteration of meeting analysis additional meeting datais saved in the database of historical meeting data (416).

In some configurations, the executing of the natural language processingalgorithms further includes identifying the respective contexts based onan order of words within each segment in the segments.

In some configurations, the tagged segments are stored in the databaseof historical meeting data, the tagged segments forming a completeelectronic transcript of respective meetings. Likewise, in someconfigurations, the tagged segments are stored in the database ofhistorical meeting data, the tagged segments each comprising an audiorecording of the tagged segment and at least one tag.

In some configurations, the natural language processing algorithms canidentify speakers within the new meeting, where the segments aresegmented based upon the speakers, such that each time a change ofspeaker occurs a new segment begins, and the meeting effectiveness scoreis based, at least in part, on an amount of time a given speaker speakswithin the new meeting.

In some configurations, the meeting effectiveness score is based, atleast in part, upon a predefined metric obtained from the first user.

In some configurations the method illustrated in FIG. 4 can be augmentedto include: generating, via the processor associated with the server andbased on the meeting data, a second user profile for a second user;comparing, via the processor, the tagged segments to the second userprofile, resulting in a second comparison; and generating, via theprocessor based on the additional meeting data and the secondcomparison, a second meeting effectiveness score for the second user,wherein the second meeting effectiveness score is distinct from themeeting effectiveness score of the first user.

As another example of expanding the method illustrated in FIG. 4 , themethod can further include generating, based upon the meetingeffectiveness score, a recommendation for a subsequent meeting topic.

In yet another example of expanding the method of FIG. 4 , the methodcan further include: generating, based upon the meeting effectivenessscore, a recommendation for a subsequent meeting with a specifiedindividual.

As still another example of augmenting the method illustrated in FIG. 4, the method can further include generating, based on the meetingeffectiveness score and user profiles of additional users, arecommendation for the first user to engage in a team meeting with theadditional users, the user profiles of additional users generated basedupon meeting interactions of the additional users.

With reference to FIG. 5 , an exemplary system includes ageneral-purpose computing device 500, including a processing unit (CPUor processor) 520 and a system bus 510 that couples various systemcomponents including the system memory 530 such as read-only memory(ROM) 540 and random access memory (RAM) 550 to the processor 520. Thesystem 500 can include a cache of high-speed memory connected directlywith, in close proximity to, or integrated as part of the processor 520.The system 500 copies data from the memory 530 and/or the storage device560 to the cache for quick access by the processor 520. In this way, thecache provides a performance boost that avoids processor 520 delayswhile waiting for data. These and other modules can control or beconfigured to control the processor 520 to perform various actions.Other system memory 530 may be available for use as well. The memory 530can include multiple different types of memory with differentperformance characteristics. It can be appreciated that the disclosuremay operate on a computing device 500 with more than one processor 520or on a group or cluster of computing devices networked together toprovide greater processing capability. The processor 520 can include anygeneral purpose processor and a hardware module or software module, suchas module 1 562, module 2 564, and module 3 566 stored in storage device560, configured to control the processor 520 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. The processor 520 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

The system bus 510 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. A basicinput/output (BIOS) stored in ROM 540 or the like, may provide the basicroutine that helps to transfer information between elements within thecomputing device 500, such as during start-up. The computing device 500further includes storage devices 560 such as a hard disk drive, amagnetic disk drive, an optical disk drive, tape drive or the like. Thestorage device 560 can include software modules 562, 564, 566 forcontrolling the processor 520. Other hardware or software modules arecontemplated. The storage device 560 is connected to the system bus 510by a drive interface. The drives and the associated computer-readablestorage media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputing device 500. In one aspect, a hardware module that performs aparticular function includes the software component stored in a tangiblecomputer-readable storage medium in connection with the necessaryhardware components, such as the processor 520, bus 510, display 570,and so forth, to carry out the function. In another aspect, the systemcan use a processor and computer-readable storage medium to storeinstructions which, when executed by the processor, cause the processorto perform a method or other specific actions. The basic components andappropriate variations are contemplated depending on the type of device,such as whether the device 500 is a small, handheld computing device, adesktop computer, or a computer server.

Although the exemplary embodiment described herein employs the hard disk560, other types of computer-readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, digital versatile disks, cartridges, random access memories(RAMs) 550, and read-only memory (ROM) 540, may also be used in theexemplary operating environment. Tangible computer-readable storagemedia, computer-readable storage devices, or computer-readable memorydevices, expressly exclude media such as transitory waves, energy,carrier signals, electromagnetic waves, and signals per se.

To enable user interaction with the computing device 500, an inputdevice 590 represents any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, speech and so forth. An outputdevice 570 can also be one or more of a number of output mechanismsknown to those of skill in the art. In some instances, multimodalsystems enable a user to provide multiple types of input to communicatewith the computing device 500. The communications interface 580generally governs and manages the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Use of language such as “at least one of X, Y, and Z,” “at least one ofX, Y, or Z,” “at least one or more of X, Y, and Z,” “at least one ormore of X, Y, or Z,” “at least one or more of X, Y, and/or Z,” or “atleast one of X, Y, and/or Z,” are intended to be inclusive of both asingle item (e.g., just X, or just Y, or just Z) and multiple items(e.g., {X and Y}, {X and Z}, {Y and Z}, or {X, Y, and Z}). The phrase“at least one of” and similar phrases are not intended to convey arequirement that each possible item must be present, although eachpossible item may be present.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. Various modifications and changes may be made to theprinciples described herein without following the example embodimentsand applications illustrated and described herein, and without departingfrom the spirit and scope of the disclosure.

We claim:
 1. A method comprising: generating a user profile for a userbased on transcripts of a plurality of meetings in which the userparticipated; receiving additional meeting data for a new meeting inwhich the first user participated; processing, using a machine learningprocess, a transcript of the additional meeting data into transcriptsegments and a set of tags associated with the transcript segments,wherein the set of tags is determined by tagging each respective segmentof the transcript segments based on a respective context of therespective segment; generating a meeting effectiveness score based onthe set of tags and the user profile by comparing the set of tags to theuser profile; updating, based on the meeting effectiveness score andcommon patterns within the transcript segments, the machine learningprocess to improve an ability of the machine learning process togenerate the transcript segments; and storing values of the updatedmachine learning process in a memory.
 2. The method of claim 1, whereinusing the machine learning process comprises, for a respective segmentof the transcript segments, identifying a respective context of therespective segment based on an order of words within the respectivesegment.
 3. The method of claim 1, wherein the machine learning processaccesses a database of historical meeting data, and wherein with eachiteration of meeting analysis additional meeting data is saved in thedatabase of historical meeting data, and wherein tagged segmentscomprises the set of tags and the transcript segments, and wherein thetagged segments are stored in the database of historical meeting data,the tagged segments forming a complete electronic transcript ofrespective meetings.
 4. The method of claim 1, wherein the machinelearning process accesses a database of historical meeting data, andwherein with each iteration of meeting analysis additional meeting datais saved in the database of historical meeting data, and wherein taggedsegments comprises the set of tags and the transcript segments, andwherein the tagged segments are stored in the database of historicalmeeting data, the tagged segments each comprising an audio recording ofa tagged segment of the tagged segments and at least one tag.
 5. Themethod of claim 1, wherein: the machine learning process identifiesspeakers within the new meeting; the transcript segments are segmentedbased upon the speakers, such that each time a change of speaker occursa new segment begins; and the meeting effectiveness score is based on anamount of time a given speaker speaks within the new meeting.
 6. Themethod of claim 1, wherein the user is a first user, and wherein themeeting effectiveness score is based upon a predefined metric obtainedfrom the first user.
 7. The method of claim 1, further comprising:generating a second user profile for a second user; comparing the set oftags to the second user profile, resulting in a second comparison; andgenerating, based on the additional meeting data and the secondcomparison, a second meeting effectiveness score for the second user,wherein the second meeting effectiveness score is distinct from themeeting effectiveness score of the user.
 8. The method of claim 1,further comprising: generating, based upon the meeting effectivenessscore, a recommendation for a subsequent meeting topic.
 9. The method ofclaim 1, further comprising: generating, based upon the meetingeffectiveness score, a recommendation for a subsequent meeting with aspecified individual.
 10. The method of claim 1, further comprising:generating, based on the meeting effectiveness score and user profilesof additional users, a recommendation for the first user to engage in ateam meeting with the additional users, the user profiles of theadditional users generated based upon meeting interactions of theadditional users.
 11. The method of claim 1, wherein processing thetranscript into segments comprises: determining a set of audio segmentsbased on audio data of the new meeting; determining an initial set ofcontexts based on the set of audio segments by, for each respectiveaudio segment of the set of audio segments, determining a respectivecontext of the respective audio segment; detecting a set of contextchanges between different portions of the initial set of contexts; anddetermining the transcript segments based on the set of context changes.12. A system comprising: a processor; and a non-transitorycomputer-readable storage medium having instructions stored which, whenexecuted by the processor, cause the processor to perform operationscomprising: using a machine learning process to perform natural languageprocessing on an audio input of a meeting between a plurality ofindividuals in which a speaker participates to obtain a plurality ofsegments and a set of contexts associated with the plurality ofsegments; generating a meeting score based on a user profile and the setof contexts by comparing the user profile to the set of contexts or aset of tags derived from the set of contexts; updating weights of themachine learning process to reduce a computational cost of obtaining theplurality of segments based on the meeting score and common patternswithin the plurality of segments; and storing the weights of the updatedmachine learning process in a memory.
 13. The system of claim 12,wherein updating weights of the machine learning process is based on atleast one of a context of the meeting, a speaking party within themeeting, a relative time period within the meeting, or a style ofpresentation.
 14. The system of claim 13, wherein updating weights ofthe machine learning process comprises by accessing a database ofhistorical meeting data, wherein with each iteration of meeting analysisadditional meeting data is saved in the database of historical meetingdata.
 15. The system of claim 14, wherein the machine learning processaccesses a database of historical meeting data, and wherein with eachiteration of meeting analysis additional meeting data is saved in thedatabase of historical meeting data, and wherein tagged segmentscomprises the set of tags and the plurality of segments, and wherein thedatabase of historical meeting data stores tagged segments, the taggedsegments each comprising an audio recording and at least onecorresponding tag.
 16. The system of claim 12, wherein the meeting scoreis further based on an amount of time a given speaker speaks within themeeting.
 17. The system of claim 12, wherein the user profile is aprofile of a user, and wherein the meeting score is further based upon apredefined metric obtained from the user.
 18. The system of claim 12,the non-transitory computer-readable storage medium having additionalinstructions stored which, when executed by the processor, cause theprocessor to perform operations comprising: generating, based upon themeeting score, a recommendation for a user to engage in a subsequentmeeting topic, wherein the user profile is a profile of the user. 19.The system of claim 12, the non-transitory computer-readable storagemedium having additional instructions stored which, when executed by theprocessor, cause the processor to perform operations comprising:generating, based upon the meeting score, a recommendation for a user toengage in a subsequent meeting with a specified individual, wherein theuser profile is a profile of the user.
 20. The system of claim 12, thenon-transitory computer-readable storage medium having additionalinstructions stored which, when executed by the processor, cause theprocessor to perform operations comprising: changing a scheduled futuremeeting based on the meeting score.
 21. A non-transitorycomputer-readable storage medium having instructions stored which, whenexecuted by a processor, cause the processor to perform operationscomprising: receiving a meeting score, wherein the meeting score isgenerated by: performing natural language processing on audio input froma meeting, resulting in a transcript of the audio input; using a machinelearning process based on the transcript of the audio input to determinea set of segments and a set of contexts for each respective segment ofthe set of segments; comparing a user profile of a user to the set ofsegments and the set of contexts to determine a comparison; andgenerating the meeting score of the meeting based on the comparison;updating weights of the machine learning process to reduce acomputational cost of obtaining the set of segments based on the meetingscore and common patterns within the set of segments; and storing theweights of the updated machine learning process in a memory.